Inverse protein folding is challenging due to its inherent one-to-many
mapping characteristic, where numerous possible amino acid sequences can fold
into a single, identical protein backbone. This task involves not only
identifying viable sequences but also representing the sheer diversity of
potential solutions. However, existing discriminative models, such as
transformer-based auto-regressive models, struggle to encapsulate the diverse
range of plausible solutions. In contrast, diffusion probabilistic models, as
an emerging genre of generative approaches, offer the potential to generate a
diverse set of sequence candidates for determined protein backbones. We propose
a novel graph denoising diffusion model for inverse protein folding, where a
given protein backbone guides the diffusion process on the corresponding amino
acid residue types. The model infers the joint distribution of amino acids
conditioned on the nodes' physiochemical properties and local environment.
Moreover, we utilize amino acid replacement matrices for the diffusion forward
process, encoding the biologically-meaningful prior knowledge of amino acids
from their spatial and sequential neighbors as well as themselves, which
reduces the sampling space of the generative process. Our model achieves
state-of-the-art performance over a set of popular baseline methods in sequence
recovery and exhibits great potential in generating diverse protein sequences
for a determined protein backbone structure.

提出了一种新颖的图去噪扩散模型，使用氨基酸替代矩阵对扩散过程进行编码，并在多种基准方法中实现了最先进的序列恢复性能，为特定的蛋白质骨架结构生成多样性的蛋白序列具有极大的潜力。

逆蛋白质折叠的图形去噪扩散

Graph Denoising Diffusion for Inverse Protein Folding

Inverse protein folding -- the task of predicting a protein sequence from its
backbone atom coordinates -- has surfaced as an important problem in the "top
down", de novo design of proteins. Contemporary approaches have cast this
problem as a conditional generative modelling problem, where a large generative
model over protein sequences is conditioned on the backbone. While these
generative models very rapidly produce promising sequences, independent draws
from generative models may fail to produce sequences that reliably fold to the
correct backbone. Furthermore, it is challenging to adapt pure generative
approaches to other settings, e.g., when constraints exist. In this paper, we
cast the problem of improving generated inverse folds as an optimization
problem that we solve using recent advances in "deep" or "latent space"
Bayesian optimization. Our approach consistently produces protein sequences
with greatly reduced structural error to the target backbone structure as
measured by TM score and RMSD while using fewer computational resources.
Additionally, we demonstrate other advantages of an optimization-based approach
to the problem, such as the ability to handle constraints.

使用深度贝叶斯优化来优化难以预测蛋白质序列的反折叠问题，并可应用于其他有约束的情况，能在减少计算资源的情况下产生具有大大降低结构误差的蛋白质序列。